Your browser doesn't support javascript.
loading
Demystifying estimands in cluster-randomised trials.
Kahan, Brennan C; Blette, Bryan S; Harhay, Michael O; Halpern, Scott D; Jairath, Vipul; Copas, Andrew; Li, Fan.
Affiliation
  • Kahan BC; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK.
  • Blette BS; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, USA.
  • Harhay MO; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK.
  • Halpern SD; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Jairath V; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Copas A; Department of Medicine, Division of Gastroenterology, Schulich School of Medicine, Western University, London, ON, Canada.
  • Li F; Department of Epidemiology and Biostatistics, Western University, London, ON, Canada.
Stat Methods Med Res ; 33(7): 1211-1232, 2024 Jul.
Article in En | MEDLINE | ID: mdl-38780480
ABSTRACT
Estimands can help clarify the interpretation of treatment effects and ensure that estimators are aligned with the study's objectives. Cluster-randomised trials require additional attributes to be defined within the estimand compared to individually randomised trials, including whether treatment effects are marginal or cluster-specific, and whether they are participant- or cluster-average. In this paper, we provide formal definitions of estimands encompassing both these attributes using potential outcomes notation and describe differences between them. We then provide an overview of estimators for each estimand, describe their assumptions, and show consistency (i.e. asymptotically unbiased estimation) for a series of analyses based on cluster-level summaries. Then, through a re-analysis of a published cluster-randomised trial, we demonstrate that the choice of both estimand and estimator can affect interpretation. For instance, the estimated odds ratio ranged from 1.38 (p = 0.17) to 1.83 (p = 0.03) depending on the target estimand, and for some estimands, the choice of estimator affected the conclusions by leading to smaller treatment effect estimates. We conclude that careful specification of the estimand, along with an appropriate choice of estimator, is essential to ensuring that cluster-randomised trials address the right question.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Randomized Controlled Trials as Topic Limits: Humans Language: En Journal: Stat Methods Med Res / Stat. methods med. res / Statistical methods in medical research Year: 2024 Document type: Article Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Randomized Controlled Trials as Topic Limits: Humans Language: En Journal: Stat Methods Med Res / Stat. methods med. res / Statistical methods in medical research Year: 2024 Document type: Article Country of publication: